Automation is more accessible than ever.
Workflow tools, automatic ticket routing, AI-assisted categorization, automated change approvals. Technically, we can automate almost everything.
But the real question is not what we can automate.
The real question is what we should automate.
In IT service management — especially in ticket handling and Change Management — automation often brings speed. But speed is not the same as improvement.
Automating an Unclear Process
Let’s take Change Management as an example.
An organization implements a workflow:
changes are automatically assigned to a coordinator,
approval requests are triggered automatically,
implementation tasks are generated after approval,
changes are automatically closed after completion.
On paper, it looks structured and controlled.
But before automation, were these questions answered?
Who owns the risk of the change?
Who has final decision authority?
What qualifies as a standard change versus a high-risk change?
Who can stop a change if something looks wrong?
If these questions were never clarified, automation only accelerates confusion.
The system runs.
The process does not.
Automation amplifies whatever already exists.
If the process is weak, automation makes it weaker — just faster.
Notification Fatigue and Parallel Approvals
In ticketing and change workflows, organizations often configure:
notifications on every status update,
automatic escalations after time thresholds,
parallel approvals from multiple stakeholders.
In theory, this increases control.
In reality:
approvers receive dozens of emails daily,
decisions happen outside the system (calls, chats),
the tool becomes a formal trace — not a management instrument.
The same happens in ticket handling.
If every update triggers a notification, people stop paying attention.
Automation without prioritization creates noise.
And noise erodes trust in the system.
Automation Without Clear Support Structure
Many organizations operate with three levels of support:
Level 1 (L1) – first-line support handling standard, repeatable requests.
Level 2 (L2) – technical specialists solving more complex issues.
Level 3 (L3) – expert teams or external vendors.
If tickets are automatically assigned without clearly defined responsibilities:
L1 forwards to L2,
L2 sends it back for missing information,
L3 waits for clarification.
The “ping-pong effect” does not disappear.
It simply becomes faster.
Automation works only when it is clear:
which level handles which types of requests,
where responsibility begins and ends,
who communicates with the end user,
who owns the service.
Without that structure, routing rules just move the problem around.
SLA and Measurability — Without Metrics, Automation Is Blind
SLA (Service Level Agreement) defines expected service performance.
Examples:
Incident response time: 30 minutes
Resolution time for critical incident: 4 hours
Approval time for standard change: 1 business day
Organizations often automate escalations when SLA thresholds are exceeded.
But if they do not:
measure real performance consistently,
analyze the reasons behind SLA breaches,
track time spent in each workflow stage,
evaluate handovers between support levels,
automation becomes reactive noise rather than performance management.
The right question is not:
“Do we have automated escalations?”
The right question is:
“Did our measurable performance improve?”
For example:
Did average resolution time decrease by 20%?
Did the number of cross-level transfers decrease?
Did the volume of emergency changes drop?
Did change success rate improve?
If the metrics do not improve, automation may be addressing symptoms — not root causes.
AI in Ticketing and Change Management
AI can:
categorize tickets,
suggest solutions,
recommend change approvals based on historical data,
identify patterns in recurring incidents.
That is powerful.
But if AI:
automatically closes tickets without human validation,
recommends approval of risky changes without context,
reassigns requests without understanding impact,
who is accountable?
AI should support decision-making — not replace it.
In Change Management especially, auditability and accountability are critical.
When AI assists a change coordinator in assessing risk, it adds value.
When AI makes the decision itself, governance becomes blurred.
When Automation Works Extremely Well
Automation is highly effective when:
the process is stable and clearly defined,
roles and responsibilities are explicit,
there is a clear process owner,
objectives are measurable.
Example in ticketing:
If a specific request type always follows a standardized procedure, automatic routing and predefined templates can significantly reduce handling time without increasing risk.
Example in Change Management:
If standard changes are well-defined and low-risk, simplified automated approval can safely reduce administrative overhead.
In these cases, automation amplifies stability — not chaos.
Responsible Automation
Before automating any part of ticketing or Change Management, organizations should ask:
Is the process stable or still evolving?
Are responsibilities clearly defined?
Is there a clear owner of this workflow?
What happens if automation fails?
How will we measure success?
Automation is not a strategy.
It is a tool.
If the goal is simply “to implement AI” or “to have workflow automation,” complexity increases without guaranteed improvement.
If the goal is:
reducing risk in Change Management,
improving SLA performance,
increasing service transparency,
reducing unnecessary escalations,
automation can be a strong accelerator.
Conclusion
In IT environments, pressure for efficiency is natural.
Automation is a logical step.
But in ticketing and Change Management, one principle remains true:
Automation does not fix a broken process.
It only makes it run faster.
And fast chaos is still chaos.
The real question is not whether we automate.
The real question is:
Are we automating the process —
or just the problem?